Graph Representation Learning for Microarchitecture Design Space Exploration - Yi, Xiaoling; Lu, Jialin; Xiong, Xiankui; Xu, Dong; Shang, Li; Yang, Fan (2023 60th ACM/IEEE Design Automation Conference (DAC), 2023)
Design optimization of modern microprocessors is a complex task due to the exponential growth of the design space. This work presents GRL-DSE, an automatic microarchitecture search framework based on graph embeddings. GRL-DSE uses graph representation learning to build a compact and continuous embedding space. Multi-objective Bayesian optimization using an ensemble surrogate model conducts microarchitecture design space exploration in the graph embedding space to efficiently and holistically optimize performance-power-area (PPA) objectives. Experimental studies on RISC-V BOOM show that GRLDSE outperforms previous techniques by 74.59% on Pareto front quality and outperforms manual designs in terms of PPA.